Abstract

Background

A growing popularity of machine learning methods application in virtual screening,
in both classification and regression tasks, can be observed in the past few years.
However, their effectiveness is strongly dependent on many different factors.

Results

In this study, the influence of the way of forming the set of inactives on the classification
process was examined: random and diverse selection from the ZINC database, MDDR database
and libraries generated according to the DUD methodology. All learning methods were
tested in two modes: using one test set, the same for each method of inactive molecules
generation and using test sets with inactives prepared in an analogous way as for
training. The experiments were carried out for 5 different protein targets, 3 fingerprints
for molecules representation and 7 classification algorithms with varying parameters.
It appeared that the process of inactive set formation had a substantial impact on
the machine learning methods performance.

Conclusions

The level of chemical space limitation determined the ability of tested classifiers
to select potentially active molecules in virtual screening tasks, as for example
DUDs (widely applied in docking experiments) did not provide proper selection of active
molecules from databases with diverse structures. The study clearly showed that inactive
compounds forming training set should be representative to the highest possible extent
for libraries that undergo screening.